Field of the Invention
This invention relates to transit logistics and, more particularly, to predicting characteristics of transit between source locations and destination locations.
Description of the Related Art
In the course of commerce, manufacturing and other business activities, different kinds of material often need to be conveyed from one location to another. For example, a global, web-based or brick-and-mortar retail sales operation may routinely ship packages containing customer orders around the world. Similarly, a distributed manufacturing operation may ship components or partially-assembled items from one manufacturing site to another for continued processing. Materials conveyance may also occur on a smaller scale, such as from a materials receiving area of a large, complex manufacturing site to one of a number of processing areas within the site.
Reliably predicting a transit characteristic, such as the time required to convey materials from one location to another (also referred to as transit time or transit latency), may be a critical parameter in an enterprise's operations. For example, in a manufacturing operation, overestimating transit time may result in having to hold materials in inventory until they are expected to be used, which may incur various logistical, facilities and overhead costs. By contrast, underestimating transit time may result in manufacturing downtime if reserves of materials are not available, which may result in lost productivity, missed production deadlines, etc. Similarly, a retail operation may find itself unable to meet customer demand or may suffer other problems with its supply chain to the extent that its supply-chain transit latencies remain unpredictable.
However, as the number of possible sources and destinations for materials increases, the number of possible transit paths to be predicted and managed quickly becomes intractable. For example, given M source locations that can ship materials to N possible different destinations, the number of possible transit paths is on the order of MN, and may be even higher if multiple different carriers or transit modalities are considered. For a retailer that ships directly to customers' businesses or residences, the number of possible destinations may number in the tens or hundreds of millions, resulting in correspondingly many potential data points to be stored. Searching through such a large number of data points to predict transit time for a particular source and destination consequently may be prohibitively expensive. Further, for a given source and destination, no historical data points may exist from which to predict transit time, or the data may be insufficient in quality or quantity for a meaningful prediction.